Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

Butt, Umair Muneer; Letchmunan, Sukumar; Hafinaz, Fadratul; Zia, Sultan; Baqir, Anees

doi:10.14569/ijacsa.2020.0110285

Cited by 21 publications

(8 citation statements)

References 22 publications

(30 reference statements)

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“…It is difficult to consecutively monitor cameras videos that recorded in public places for the detection any abnormal event so an automatic video detection system is needed for that purpose. Numerous researchers have created intelligent surveillance systems, but none of them have achieved perfect detection and accuracy rate (Butt et al, 2020).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Towards Trustworthy Human Suspicious Activity Detection from Surveillance Videos Using Deep Learning

Buttar

Bano

Akbar

et al. 2022

Preprint

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In today’s world suspicious or unusual activities express threat and danger to others. For the prevention from various security issues an automatic video detection system is very important. The study objective is to create an intelligent system that will take a video stream as input and detect what kind suspicious activity is happening in that particular video to reduce the time that consume on watching video. It is difficult to consecutively monitor cameras videos that recorded in public places for the detection any abnormal event so an automatic video detection system is needed for that purpose. For that purpose, deep learning-based model is the best approach. In this work we use three models Convolutional neural network (CNN) model GRU model and ConvLSTM model. These models are trained on the same dataset of 6 suspicious activities of humans that are (Running, Punching, Falling, Snatching, Kicking and Shooting). The dataset consist of various video related to each activity. Different deep learning techniques are applied in the proposed work that are preprocessing, data annotation model training and classification. The frames are extracted from the source video and then features are calculated through model known as Inception v3 which is a variant of Convolutional Neural Network. On the same dataset the CNN model attains 91.55% accuracy the ConvLSTM model attain 88.73% accuracy and the GRU model attain 84.01% accuracy. The performance of proposed models are evaluated using confusion matrix, f1-score, precision, and recall. The proposed model proved better than other models in terms of performance and accuracy. The findings of this study prove helpful unusual event by examining the abnormal behaviour of person.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Towards Trustworthy Human Suspicious Activity Detection from Surveillance Videos Using Deep Learning

Buttar

Bano

Akbar

et al. 2022

Preprint

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“…In addition, pre-trained CNN models that utilize transfer learning offer alternative methods that can provide better outcomes, faster training, and are also suitable for processing fewer input data. Transfer learning can be used on two types of pre-trained CNNs: series networks that include AlexNet, VGG-16, and VGG-19, and directed acyclic graph (DAG) networks, such as GoogLeNet, Inception-v3, ResNet-18, ResNet-50, and ResNet-101 [8][9][10][11][12][13][14]. Technically, transfer learning takes the knowledge of pre-trained CNNs that have previously learned on a large dataset and applies it to discover new, related, or even complete tasks in different domains.…”

Section: Introductionmentioning

confidence: 99%

“…Researchers in crime surveillance and detection heavily rely on anomalous data to help them improve their results [5,8,16]. Anomaly detection is a method or process for identifying behavior that deviates from normal behavior, which can be complex and diverse from normal behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…This tactic involves a partnership between two individuals, one handling the motorcycle and the other stealing from the intended victim [3]. For instance, [8] used 21 videos as a database. The researchers employed the VGG19 convolution neural network (CNN) to distinguish large-scale images or videos of snatch theft crimes and achieved 81% accuracy in identifying snatch theft.…”

Section: Introductionmentioning

confidence: 99%

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Real Time Snatch Theft Detection using Deep Learning Networks

Zamri¹,

Tahir²,

ALI³

et al. 2023

ARASET

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Snatch theft is a common crime in urban areas that poses a serious threat to public safety. It involves forcefully grabbing a victim's personal belongings, such as purses or mobile phones, before quickly fleeing the scene. Detecting snatch theft incidents in real-time is a challenging task due to the speed at which they occur. The current methods used to detect snatch theft incidents rely heavily on human intervention, which can lead to significant delays and potential errors. Therefore, there is a need for an automated technique that can accurately and efficiently detect these incidents in real-time. Hence, the study aims to detect snatch theft using a transfer learning approach based on eight pre-trained convolutional neural networks (CNNs) as classifiers: AlexNet, VGG16, VGG19, GoogleNet, InceptionV3, ResNet-18, ResNet-50, and ResNet-101. The modified pre-trained CNN models are evaluated in both offline and real-time modes. Based on the offline mode, VGG19 achieved 100% training accuracy, and ResNet50 had the highest testing accuracy of 98.9%. In the offline mode, all models accurately classified normal scenes, with ResNet-10 having the lowest false negative rate and ResNet-50 achieving the lowest false positive rate with only 44 misclassified anomaly frames related to snatch theft. The study further evaluated and validated the eight models in real-time mode, and the results showed that AlexNet and ResNet-18 were the only models capable of categorizing snatch theft scenarios with promising findings.

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“…In the end, there are two fully connected layers, followed by a softmax for output [57,58]. VGG19 differs from VGG16 in that it contains an extra layer in the three convolutional blocks [59].…”

mentioning

confidence: 99%

Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning

et al. 2023

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The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset.

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Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

Cited by 21 publications

References 22 publications

Towards Trustworthy Human Suspicious Activity Detection from Surveillance Videos Using Deep Learning

Towards Trustworthy Human Suspicious Activity Detection from Surveillance Videos Using Deep Learning

Real Time Snatch Theft Detection using Deep Learning Networks

Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning

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